Updating model sizes and disclaimer

README.md

@@ -32,14 +32,17 @@ This HuggingFace organization hosts our pre-trained models and datasets, while t
 ### **1. Pre-trained Model Suite**
 
 Our complete suite of models from 10M to 500M parameters trained with Pico:
-- [**pico-decoder-tiny**](https://huggingface.co/pico-lm/pico-decoder-tiny) (10M parameters) 
-- [**pico-decoder-small**](https://huggingface.co/pico-lm/pico-decoder-small) (50M parameters)
-- [**pico-decoder-medium**](https://huggingface.co/pico-lm/pico-decoder-medium) (200M parameters)
-- [**pico-decoder-large**](https://huggingface.co/pico-lm/pico-decoder-large) (500M parameters)
+- [**pico-decoder-tiny**](https://huggingface.co/pico-lm/pico-decoder-tiny) (11M parameters) 
+- [**pico-decoder-small**](https://huggingface.co/pico-lm/pico-decoder-small) (65M parameters)
+- [**pico-decoder-medium**](https://huggingface.co/pico-lm/pico-decoder-medium) (181M parameters)
+- [**pico-decoder-large**](https://huggingface.co/pico-lm/pico-decoder-large) (570M parameters)
 
-> 🚧 **Coming Soon!** **pico-decoder-xl** (1B parameters) Watch this space or star our [GitHub repository](https://github.com/pico-lm) for updates!
+> 🚧 **Disclaimer** These models are still under construction. The models released in this repository have been trained for 50,000 steps (corresponding to 100B tokens). Training will finalize after 200,000 steps.
+> 
+> 🚧 **Coming Soon!** **pico-decoder-xl** (1B+ parameters) Watch this space or star our [GitHub repository](https://github.com/pico-lm) for updates!
 
-All models are trained for 50,000 steps on the [**pretokenized-dolma**](https://huggingface.co/datasets/pico-lm/pretokenized-dolma) dataset (corresponding to 100B tokens). They all see the same training data at each training step, use the same optimizatation process, and share the same model architecture; the only difference between models is the size of their hidden dimension.
+
+All models are on the [**pretokenized-dolma**](https://huggingface.co/datasets/pico-lm/pretokenized-dolma) dataset. They all see the same training data at each training step, use the same optimizatation process, and share the same model architecture; the only difference between models is the size of their hidden dimension.
 
 In each model repository, we version control checkpoints every 1000 steps that contain:
   - Weights and optimizer states (HuggingFace and Lightning Fabric-compatible versions)